Nonnegative Matrix
Area Correlated Spectral Unmixing Based on Bayesian Nonnegative Matrix Factorization
... To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix ...
6
A study of blind source separation using nonnegative matrix factorization
... Table-1 explained that the different type of NMF used in BSS for various applications. Most of the application is based on the concept of audio or sound separation. It can be shown from the journal above that is number ...
7
Nonnegative matrix analysis for data clustering and compression
... Nonnegative matrix factorization (NMF) enables approximation of data vectors by linear combinations of a small set of nonnegative basis vectors weighted by nonnegative ...
162
Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization
... In this paper, we propose a model-independent method- ology to study spatiotemporal patterns between neuronal spikes and behavior utilizing nonnegative matrix factoriza- tion (NMF) [9, 10]. In its original ...
9
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
... Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, under which all the columns of the input data matrix be- long to the convex cone ...
32
A Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
... Since nonnegative matrix factorization is an non- convex problem, so initialization of factor matrices for NMF algorithms is very important. Initialization techniques should be able to provide good ...
8
Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization
... supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion ...
16
On rationality of nonnegative matrix factorization
... the nonnegative ranks over R and Q coincide for matrices of rank at most ...of nonnegative rank (over R ) at most ...the nonnegative rank of a matrix can indeed depend on the underlying field: ...
17
A Robust Symmetric Nonnegative Matrix Factorization Framework for Clustering Multiple Heterogeneous Microbiome Data
... example, Nonnegative Matrix Factorization (NMF) adopts relative entropy to measure the signal compression ratio and obtain good ...data matrix and the low-rank approximation reflects the maximum ...
14
Fast Nonnegative Matrix Factorization and Its Application for Protein Fold Recognition
... via matrix factorization with nonnegativity constraints is ...explore nonnegative matrix fac- torization in combination with three nearest-neighbor classifiers for protein fold ...typically ...
8
Nonnegative Matrix Factorization with Zellner Penalty
... Zellner nonnegative matrix factorization ...Constrained nonnegative matrix factorization (CNMF) [8] and show that it is superior across all selected factorization ...
10
Nonnegative matrix factorization requires irrationality
... the nonnegative ranks over R and Q coincide for matrices of rank at most ...of nonnegative rank (over R ) at most ...the nonnegative rank of a matrix can indeed depend on the underlying field: ...
23
Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
... We present a numerical algorithm for nonnegative matrix factorization (NMF) problems under noisy separability. An NMF problem under separability can be stated as one of finding all vertices of the convex ...
29
Robust Multi Relational Clustering via ℓ1 Norm Symmetric Nonnegative Matrix Factorization
... Traditional clustering aims to partition data points into several groups, such that the data points in the same group can share some commonalities whilst those from different groups are dissimilar. With the recent ...
5
Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing
... Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representa- ...the ...
38
Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization
... adjacency matrix; and ‘x’ indicates that an approxi- mated normalized cut is computed using the normalized adjacency matrix, which usually results in faster ...adjacency matrix, which does not have a ...
26
Some new sharp bounds for the spectral radius of a nonnegative matrix and its application
... In this paper, we give some new sharp upper and lower bounds for the spectral radius of a nonnegative irreducible matrix. Using these bounds, we obtain some new and improved bounds for the signless ...
6
Community Detection in Temporal Networks Using Triple Nonnegative Matrix Factorization
... • By introducing node weight matrices, our model can target central nodes of communities and reduce number of nodes that have unobvious propensities of belonging to communities, which im[r] ...
7
Monotonicity of the number of positive entries in nonnegative matrix powers
... A matrix is nonnegative (positive) if all of its entries are nonnegative (positive) real num- ...bers. Nonnegative matrices have many attractive properties and are important in a variety of ...
5